1. Field of the Invention
The present invention relates generally to surveillance, and more particularly to an apparatus and method for detecting and tagging changes in a scene captured by a camera or a similar sensing device.
2. Description of Related Art
Visual surveillance is an integral task in many fields, including, but not limited to, manufacturing quality inspection, monitoring of vehicles or other objects on or near a road from a distance point not within the vehicle or from a moving vehicle, security monitoring of parking lots and entrance points to a buildings, medical monitoring of changes to a surface or image (e.g., x-ray images, images of the skin, etc.), environmental monitoring of changes to a surface or image (e.g., remote or aerial views of terrestrial landscape), monitoring of objects in the air, sky or outer space, and other similar tasks. While it may be sufficient in some cases to perform sample surveillance or inspection, where only a portion of the images of an area or location or a subset of objects is examined, in others, such as inspection of manufactured printed circuit boards, a sample inspection is not desirable. Further, in some cases, a human inspection process can be labor-intensive and time consuming. As such, automated surveillance or inspection techniques are desirable.
Automated surveillance approaches can include either referential or non-referential techniques. Referential surveillance includes the use of template or model matching while non-referential approaches include rule-based or morphological techniques. Both of these techniques can be computational intensive, can require significant time to setup, and can suffer from problems such as lighting and material variations. The usefulness of non-referential techniques also depends on the development of a sufficient number of rules to check for inconsistencies. Special image acquisition techniques and various post-processing tools have been developed to assist the automated surveillance process. For instance, special lighting or range camera methods may be able to selectively acquire information. Further, scale-space approaches and contour maps as well as pattern recognition techniques and neural networks can improve the processing of acquired images. However, in many cases, the complexity of the surveillance task and speed of computation is still an issue.
The referenced shortcomings are not intended to be exhaustive, but rather are among many that tend to impair the effectiveness of previously known techniques concerning detecting errors or changes in a scene; however, those mentioned here are sufficient to demonstrate that the methodologies appearing in the art have not been satisfactory and that a significant need exists for the techniques described and claimed in this disclosure.